\section{Experimentation}
\subsection{Package Count Scalability Experiment}

In this experiment we will try to isolate the effect an increasing amount of packages has on the calculations the Truck Agents need to perform. As it is hard to accurately measure how long the Truck Agents are actually building/calculating/evaluating paths, we will try to manipulate our environment so that using the total calculation time of the entire simulation (which includes the actual driving) will approach an accurate reflection of the calculation time.

We will attempt to do this by flooding our environment with an abundance of trucks. This way all our packages will be delivered in one sequence or close to it, keeping the total simulation time relatively even. Also, because of the high truck count, the ratio of time spent calculating to time only spent driving becomes very high, and we manage to almost isolate the calculation time.

\subsubsection{Setup}
Our testing setup consists of the following:
\begin{description}
 \item 30 trucks deployed randomly on the map of Leuven.
 \item Only one Pickup-and-Delivery sequence considered.
 \item 20 iterations with random package locations and random truck locations for each package count, of which the average is used.
 \item Performed for package counts 5, 10, 15, 20, 25, 30.
 \item We disable truck exploration if he hasn't found a package or won an intention after reaching the maximum exploration count, so it doesn't take up too much calculation time at the end of the simulation.
 \item All other parameters are kept constant for these tests.
\end{description}
We can clearly see that our system scales pretty well with increasing packagecounts. The performance only starts declining pretty hard when packages are deployed very closely to eachother and a lot of trucks are in the vicinity of these packages.

While this scenario shows that the way our agents behave leads to good package scalability, it is not a realistic image of how our system would work when our problem would continuously feature a lot of packages. In that case, our solution provides the nifty possibility of lowering exploration and/or feasibility range to achieve calculation times very similar to those of lower package counts, but with a trade off in path optimality.


\subsection{Multiple Pickup-and-Delivery Sequences Experiment}
We would have liked to do an experiment comparing the average pickup times with a variable amount of pickup-and-delivery sequences considered, but due to reasons mentioned in Section \ref{sec:criticalreflection} we were unable to do so. We will provide a quick sketch of the setup we would have used anyway.

\subsubsection{Setup}
\begin{description}
 \item 40 environments, which consist of 5 trucks and 40 packages in fixed locations.
 \item We calculate the average pick up times for each environment for an incrementing amount of PnD sequences considered.
 \item We take the average of the average pick up times across all the environments for each amount of PnD sequences considered.
 \item We compare these last averages to get an idea of the gains looking multiple sequences ahead can give us.
 \item Lastly, we increase the amount of packages in all 40 environments and run everything again to see if these gains increase when the package count increases.
\end{description}